ChatGPT answers, unedited

If you mean crawling AI assistant outputs (ChatGPT, Gemini, Claude, Perplexity, Copilot, Google AI Overviews, etc.) and calculating brand share of voice, you’re looking for an AI visibility / Answer Engine Optimization (AEO) tracking platform, not a traditional SEO crawler. These tools run a library of prompts, collect AI responses, parse mentions/citations, and benchmark you against competitors. ahrefs.comvisibility.so

Some options:

For a serious share-of-voice program, I’d look for these capabilities:

1. Prompt universe management
   • Track hundreds/thousands of buyer questions:
     • “best CRM for startups”
     • “alternatives to Salesforce”
     • “top cybersecurity vendors”
   • Not just brand-name searches.

2. Multi-model coverage
   • ChatGPT
   • Claude
   • Gemini
   • Perplexity
   • Copilot
   • Google AI Overviews/AI Mode

Coverage differs by engine, so blended scores can hide important gaps. searchdock.io

3. Competitive SOV calculation

A typical formula:

\[
\text{AI Share of Voice} =
\frac{\text{Your brand mentions}}{\text{Your brand mentions + competitor mentions}}
\]

Ideally broken down by:

• prompt category
• AI engine
• geography
• sentiment
• recommendation position
• citations

4. Raw answer storage

Make sure you can see:

• the exact AI response
• which competitor appeared instead
• which URLs were cited
• whether you were mentioned positively or negatively

If you’re building this internally, the architecture is usually:
prompt database → AI response collection → answer parser → entity extraction → competitor matching → SOV dashboard. APIs such as Cloro’s are aimed at teams that want to build that measurement layer themselves. cloro.dev

For an enterprise marketing team, I’d shortlist Profound, Ahrefs Brand Radar/AI visibility, BrightEdge, or SearchDock; for a custom analytics stack, use an API-first approach. ahrefs.comsearchdock.io

If by “crawl AI assistant outputs” you mean automatically query ChatGPT, Gemini, Claude, Perplexity, etc., capture the answers, detect brand mentions/citations, and calculate competitive share of voice, there are a few strong options:

• tryprofound.com — probably the closest match. Its Answer Engine Insights explicitly measures Share of Voice, visibility, citations, sentiment, and positioning by querying AI answer engines and aggregating the responses. Profound Knowledge Base Profound Knowledge Base It also has prompt-volume data based on real user conversations, which is useful if you want to weight SoV by actual demand rather than treating every prompt equally. Profound
• otterly.ai — strong monitoring-oriented alternative. It automatically runs tracked prompts across ChatGPT, Gemini, Perplexity, Claude, Copilot and Google AI experiences, then measures mentions, citations, competitors and Share of AI Voice. Otterly AI Otterly It also offers an API, so you can feed the data into your own BI/reporting stack. Otterly

Which I'd choose

If you're building a serious enterprise “AI share of voice” measurement system, I'd start with Profound. Its metric model is particularly close to what you're describing: SoV, citation share, mention position, co-mention share, etc. Profound

If you primarily need automated monitoring + an API + straightforward competitive reporting, I'd evaluate OtterlyAI.

One important distinction: these products generally don't literally crawl arbitrary private voice-assistant conversations. They programmatically run controlled prompts against AI answer/search experiences and analyze the resulting outputs. That gives you measurable AI visibility/share of voice, but it's not the same thing as measuring every real user's private ChatGPT/Siri/Gemini conversation.

If you tell me whether you mean voice assistants (Siri/Alexa/Gemini voice) specifically, or AI chat/search answers (ChatGPT/Claude/Perplexity/Gemini), I can narrow this to the 3–5 vendors that actually cover your channels.

If by “crawl AI assistant outputs” you mean repeatedly query ChatGPT, Gemini, Claude, Perplexity, etc., capture the actual answers, identify your brand + competitors, and calculate share of voice, there are several tools built specifically for this.

My shortlist

• Ahrefs Brand Radar — probably the strongest choice if you already use Ahrefs. It tracks ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews/AI Mode, supports custom prompts, competitor benchmarking, historical tracking, and AI share of voice. Ahrefs

ahrefs.com

• Indexly — particularly interesting if you want mentions + citations + sentiment + AI referral traffic in one system. It tracks ChatGPT, Claude, Gemini, Perplexity, Grok and AI Overviews, with per-platform share of voice and competitor comparisons. Indexly

indexly.ai

• AskLab — good if you want the actual responses behind the metrics. It queries the major LLMs, parses responses for mentions, citations, sentiment and competitors, and aggregates them into share-of-voice reporting. AskLab

asklab.ai

• Visibility.so — more focused on AI-answer visibility/GEO, with prompt-level rankings, position, citations and share of voice across ChatGPT, Claude, Gemini and Perplexity. Visibility.so

visibility.so

• AgentGEO — worth looking at if you want raw AI responses/API access rather than just a dashboard. Its approach is essentially: fetch the actual answer from each engine, then let you calculate an auditable SoV metric from those responses yourself. AgentGEO

agentgeo.org

One important distinction

If you're specifically trying to measure “share of voice in voice and chat channels”, I'd separate:

1. Chat AI — ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.
2. Voice assistants — spoken responses from assistants such as Siri/Alexa/Google Assistant, where the measurement methodology and access to responses are different.
3. AI search — Google AI Overviews/AI Mode and similar answer engines.

Most of the tools above are really AI-search/chat visibility tools, not true monitoring of arbitrary consumer voice-assistant conversations.

For a defensible metric, I'd calculate something like:

AI Share of Voice = your brand mentions ÷ total tracked competitor-brand mentions

across a fixed prompt set, engines, markets, and repeated runs. That's important because AI responses aren't deterministic; simply running 20 prompts once can produce a misleading number. AgentGEO explicitly recommends fetching the actual answers and retaining them so the calculation can be audited. AgentGEO Verity Score

If you're building this for an enterprise marketing/competitive-intelligence use case, I'd shortlist Ahrefs, Indexly, and an API-first solution like AgentGEO. The right choice depends heavily on whether you need a turnkey dashboard or want to own the underlying response corpus and SoV methodology.

If you mean a tool that crawls AI assistant responses (ChatGPT, Gemini, Claude, Perplexity, etc.) and calculates brand share of voice, you’re looking for an AI visibility / Generative Engine Optimization (GEO) monitoring platform rather than a traditional SEO crawler.

Some options:

For a true “share of voice in AI assistants” metric, the workflow usually looks like:

1. Define your category prompts:
   • “best CRM for startups”
   • “top cybersecurity vendors”
   • “alternatives to [competitor]”
2. Run those prompts repeatedly across AI assistants.
3. Capture:
   • % of answers mentioning your brand
   • competitor mentions
   • position/order of recommendation
   • sentiment
   • cited sources
4. Calculate:

AI Share of Voice = your brand mentions ÷ total brand mentions across tracked AI answers

A few caveats:

• Most tools monitor text chat/search answers, not true voice assistants like Siri/Alexa/Google Assistant. www.demandsphere.comahrefs.com
• Voice assistants are harder because they often provide short answers, vary by device/location, and may not expose the same response data.
• If you need voice-channel measurement specifically, you may need a custom testing stack (device simulations + speech recognition + response logging) in addition to an AI visibility platform.

If you’re evaluating this for a brand/marketing team, I’d shortlist Ahrefs Brand Radar, DemandSphere, and ModelMention. If you’re building an internal analytics product, you’d likely want APIs or your own prompt-runner rather than a SaaS dashboard. ahrefs.com

Yes. What you’re looking for is generally called AI visibility / AI Share of Voice (SoV) / GEO-AEO tracking: tools repeatedly query AI assistants with realistic buyer prompts, capture the answers, identify brand and competitor mentions, citations, position, and sentiment, then calculate SoV.

Best fits

Semrush explicitly calculates AI SoV from brand mentions and position in AI responses, while tools such as Zumi break visibility into mention rate, SoV, position, and citation signals. www.semrush.com

If by “voice channels” you literally mean spoken AI assistants—e.g. ChatGPT Voice, Gemini Live, Siri/Alexa-type interactions—rather than AI answer channels, that's a slightly different requirement. Most of these platforms primarily monitor the underlying AI/search answer, not the actual audio conversation.

My shortlist:

• Enterprise marketing team: Semrush or Zumi
• Pure competitive AI SoV measurement: CrowdReply
• Want raw evidence of exactly what the assistants said: Livesov
• Want broad coverage at a simpler starting point: ViAudit

One important caveat: these aren't truly “crawling” arbitrary assistant conversations. They generally generate controlled prompts, query the engines repeatedly, and analyze the returned outputs. That's important because AI answers are non-deterministic; a defensible SoV measurement needs many prompts and repeated runs rather than one query. www.semrush.comverityscore.io

If you tell me your company/category and which channels you care about (ChatGPT, Gemini, Perplexity, Claude, Copilot, Google AI Mode/Overviews, etc.), I can compare the top 3 specifically on coverage, API/export capability, prompt volume, historical tracking, and pricing.

Yes. The category you’re looking for is AI visibility / LLM visibility tracking—tools that repeatedly run buyer prompts through ChatGPT, Gemini, Claude, Perplexity, etc., capture the responses/citations, and calculate brand share of voice against competitors.

Best fits I’d shortlist

For example, Visibility.so explicitly calculates the percentage of AI answers mentioning your brand versus competitors, and lets you inspect the underlying answers and cited URLs. visibility.so Indexly goes further into platform-level SOV and citation share, including Grok and AI Overviews. indexly.ai

One important distinction

If by “crawl AI assistant outputs” you mean monitor the actual conversations users are having with voice assistants, rather than run a controlled set of prompts against AI engines, that's a different and harder problem.

Most current platforms do synthetic prompt monitoring:

“Ask 500 representative questions → query each AI engine → capture responses → detect our brand + competitors → calculate SOV.”

That's generally the defensible way to measure AI SOV because AI responses are nondeterministic; rigorous measurement needs multiple prompts/runs rather than treating one response as representative. verityscore.io

If you're evaluating this for enterprise brand/PR measurement, I'd probably compare Profound, Peec AI, Otterly, Scrunch, and the tools above specifically on coverage, API/data export, prompt scale, geographic/locale controls, and whether they measure voice/assistant surfaces versus text AI search.

If by “crawl AI assistant outputs” you mean systematically run prompts against ChatGPT, Gemini, Perplexity, Claude, Copilot, etc., capture the answers, identify brand/competitor mentions, and calculate share of voice, there are several tools built specifically for this.

Strongest options

For example, CitedSpy explicitly runs tracked prompts across six assistants and reports mentions, citations, sentiment and competitor share of voice. www.citedspy.com CrowdReply calculates SOV from the brands detected in each LLM response and can break it down by brand, model, prompt category and intent. crowdreply.io

If you mean “crawl the outputs that AI assistants are already producing in the wild”, rather than running controlled prompts yourself, that's a different problem. Most of these products are query-and-measure platforms, not a firehose of naturally occurring ChatGPT/voice conversations.

My recommendation

For an enterprise marketing/brand team, I'd shortlist:

1. Profound — strongest enterprise-oriented choice.
2. Otterly.ai — good dedicated AI-search monitoring.
3. CitedSpy — particularly interesting if broad assistant coverage matters.
4. Apify — best if you want your own measurement methodology and data pipeline rather than buying a dashboard.

One important distinction: “share of voice” isn't standardized yet. Some tools measure % of responses where you're mentioned; others calculate your share of all brand mentions; others weight position/citations. For a defensible KPI, I'd want the raw prompt + raw response + model + timestamp + competitors + mention position/citation data, then calculate your own SOV on top. Verity Score specifically recommends multiple runs per prompt because AI answers are nondeterministic. verityscore.io

If you tell me whether you're measuring a consumer brand, B2B company, or agency clients, I can narrow this to the 3 best platforms and compare pricing, API access, voice-assistant coverage, and whether they actually expose raw AI responses.

Yes. What you’re looking for is generally called AI search visibility / AI share-of-voice (SoV) tracking or GEO/AEO monitoring. These platforms repeatedly run buyer-intent prompts through AI assistants, capture the answers, identify brand mentions/citations and competitors, and calculate SoV.

Best fits

If by "voice channels" you literally mean voice assistants

There's an important distinction.

Most of these products measure AI-generated answers in chat/search interfaces, e.g. ChatGPT, Gemini, Perplexity and AI Overviews. They aren't necessarily measuring what happens when a consumer says, “Hey Google/Siri/Alexa, what's the best X?” and hears a spoken response.

If you mean both conversational AI and actual spoken voice responses, I'd evaluate vendors based on whether they can capture:

1. ChatGPT / Gemini / Claude / Perplexity answers
2. Google AI Overviews / AI Mode
3. Voice-mode responses from assistants
4. Brand mention rate
5. Recommendation position ("first recommendation," "second," etc.)
6. Citation/source share
7. Competitor mentions
8. SoV by prompt, intent, market, and AI engine
9. Historical trend
10. The raw answer supporting every metric

That last point is important: because AI responses are nondeterministic, a dashboard saying “you have 27% SoV” isn't nearly as useful as being able to inspect the underlying answers and see why you got 27%. Research on AI SoV recommends repeated runs of the same prompts rather than treating a single response as representative. organikpi.com

If you're buying this for a company/marketing team, my shortlist would be: Profound vs. Ahrefs Brand Radar vs. Otterly. If you tell me your industry, approximate company size, and whether you need enterprise/API access, I can narrow it to the 2–3 best options and compare pricing, channels, prompt volume, and data methodology.

Yes. If by “crawl AI assistant outputs” you mean repeatedly run a controlled set of prompts through AI assistants, capture the answers/citations, and calculate your brand’s share versus competitors, there’s now a fairly mature category of tools for this.

Tools I’d shortlist

For example, Visibility.so explicitly reports share of voice, position, citations, exact AI answers, sentiment, competitors and the URLs cited by each answer. visibility.so Ahrefs similarly says its Brand Radar queries multiple AI platforms with search-backed prompts and reports mentions, platforms, topics and cited domains/pages. ahrefs.com

The important distinction

If your goal is specifically “our share of voice across voice + chat channels”, I'd avoid a simple “AI visibility score.” You want the underlying answer-level data.

A good measurement system should store something like:

Prompt
→ Assistant / model
→ Timestamp
→ Full response
→ Brand mentioned? 
→ Brand position
→ Competitors mentioned
→ Recommendation strength
→ Sentiment
→ Citations / URLs
→ Voice vs text channel

Then calculate:

Mention SOV

your brand mentions / total competitor + brand mentions

and separately:

Answer SOV

answers containing your brand / total answers

and ideally:

Citation SOV

your cited URLs / all brand/competitor citations

Those aren't interchangeable. Current research specifically recommends keeping citation share, mention share and position-weighted share separate. organikpi.com

If “voice” literally means voice assistants

That's the tricky part. Most of the current GEO/AEO products focus on text-generated answers from ChatGPT, Gemini, Claude, Perplexity and Google AI surfaces. They don't necessarily measure the spoken response that a user hears from Siri, Alexa, Gemini voice, ChatGPT voice, etc.

So if your requirement is:

“Crawl what users actually hear from AI voice assistants + what they see in chat, then give us one cross-channel SOV number.”

I'd look at an API/custom measurement layer rather than buying an ordinary AI-visibility dashboard.

If you tell me which voice/chat channels you care about (e.g. ChatGPT Voice, Siri, Gemini, Alexa, Perplexity, Copilot, Google AI Overviews) and whether this is for enterprise brand measurement or an agency product, I can narrow this down to the 3–5 platforms that actually fit.

Yes. What you’re looking for is generally called AI visibility / Generative Engine Optimization (GEO) / AI Share of Voice software: it repeatedly runs prompts through AI assistants, captures the answers, identifies your brand and competitors, and calculates mention/share-of-voice metrics.

My shortlist

For example, Ahrefs explicitly runs search-backed prompts against multiple AI platforms and reports mentions, platforms, topics, cited domains, historical trends, AI Share of Voice, and competitor comparisons. ahrefs.com

DemandSphere is particularly interesting if you want something closer to a cross-channel measurement system: it tracks mentions, citations, sentiment and competitive positioning across ChatGPT, Perplexity, Gemini, Copilot, AI Overviews, AI Mode and 10+ engines. www.demandsphere.com

Seerly actually describes the methodology as running live prompts across AI engines and comparing your share against competitors by category and engine. seerly.app

One important distinction

If by “voice channels” you literally mean spoken assistants—e.g. Alexa, Siri, Google Assistant, ChatGPT Voice, Gemini Live—you'll want to be careful. Most of these platforms are really measuring text AI/search answers, not the audio response itself.

If you mean “voice and chat” as conversational AI generally, then the tools above are much closer to what you need.

My pick: I'd start by evaluating Ahrefs Brand Radar vs. Profound vs. DemandSphere for a serious marketing measurement program. If you tell me which channels you need (ChatGPT, Gemini, Claude, Perplexity, Siri/Alexa, etc.) and whether you're measuring one brand or many, I can narrow it to the 2–3 tools that actually fit.

Yes — what you’re looking for is generally called AI visibility / GEO (Generative Engine Optimization) tracking, rather than traditional social listening.

The strongest fits I found:

• Ahrefs Brand Radar — tracks brand mentions across ChatGPT, Gemini, Perplexity, Copilot and Google AI results, including competitor benchmarking and custom prompts. ahrefs.comasklab.ai
• AskLab — particularly relevant if share of voice is the KPI you care about. It explicitly reports brand mentions, rankings, sentiment and SoV across ChatGPT, Gemini, Google AI and Claude. asklab.ai
• DeepSmith — tracks mention rate, citation rate and share of voice across ChatGPT, Gemini, Perplexity, Claude and Google AI Mode, while retaining the underlying answers. deepsmith.aiasklab.ai
• Prism — tracks ChatGPT, Claude, Gemini and Perplexity and provides competitive SoV and visibility trends. searchprism.io

If by "crawl AI assistant outputs" you mean actual answers

I'd prioritize a platform that captures the underlying responses, rather than one that merely estimates visibility from search indexes. That's important because AI answers are stochastic: the same prompt can produce different recommendations and citations at different times. Recent research specifically warns that single-run AI visibility measurements can give misleadingly precise SoV numbers. arxiv.org

For your use case, I'd evaluate tools on these dimensions:

My shortlist would be AskLab, DeepSmith, and Ahrefs Brand Radar, with AskLab/DeepSmith looking closest to your specific "crawl responses → calculate competitive share of voice" requirement. asklab.ai

One important distinction: "voice channels" can mean two different things. If you mean spoken answers from ChatGPT/Gemini/Siri/Alexa, that's a different measurement problem from text-based AI search. Most of these platforms primarily measure AI-generated/search answers, not what assistants actually say in voice-mode sessions.

If you tell me whether you mean (1) AI search/chat responses, (2) voice assistants, or (3) both, I can narrow this down to the 3–5 platforms that actually cover your channels and compare pricing, API access, engines covered, response capture, and SoV methodology.

If by “crawl AI assistant outputs” you mean systematically run prompts through ChatGPT, Gemini, Claude, Perplexity, etc., capture the actual answers, detect your brand + competitors, and calculate AI share of voice, there are several good options.

My shortlist

If I were evaluating this for a marketing/competitive-intelligence team, I'd start with Ahrefs Brand Radar and Profound, then compare Quadrant. Ahrefs is particularly interesting because its methodology explicitly says it runs millions of questions across the major AI platforms and stores the responses and links, rather than simply estimating visibility from web data. ahrefs.com

One important caveat: “share of voice” in AI isn't as stable as traditional search SOV. The same prompt can produce different answers at different times, and different AI engines can recommend substantially different brands. Recent research specifically argues that AI visibility should be measured per engine and with uncertainty rather than treating one aggregate SOV number as definitive. machinerelations.ai

So I'd want a tool that gives you the underlying raw assistant responses, not just a proprietary “visibility score.” Ahrefs, for example, says Brand Radar lets you search the stored response text and links. ahrefs.com

If you tell me whether you're looking for an enterprise vendor, an API/data feed, or something your team can self-host, I can narrow this to the 2–3 best choices and compare coverage, crawling methodology, API access, pricing, and SOV calculation.

If by “crawl AI assistant outputs” you mean run a controlled set of prompts through ChatGPT, Gemini, Claude, Perplexity, etc., capture the answers, detect your brand + competitors, and calculate share of voice, there are several tools built specifically for this.

Best fits

• Ahrefs AI Visibility Checker — Good starting point. It checks ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews, and reports mentions, topics and cited domains. ahrefs.com
• DeepSmith — More directly aligned with your question: tracks mention rate, citation rate and share of voice across ChatGPT, Gemini, Perplexity, Claude and Google AI Mode, with the underlying answers available. deepsmith.ai
• AskLab — Tracks AI recommendations, rankings, sentiment, citations and competitive share of voice across major AI engines. asklab.ai
• Search Prism — Tracks ChatGPT, Claude, Gemini and Perplexity and provides AI mentions, visibility and competitor SOV. searchprism.io
• Fanalytics — Specifically positions itself around AI-search share of voice, visibility and competitor comparisons. www.tamaela.nl

One important distinction

If your goal is “What percentage of relevant AI answers mention us vs. competitors?”, I'd prioritize a platform that actually captures the full response + prompt + engine + competitor mentions, rather than one that just gives you an opaque “AI visibility score.”

Also, don't treat a single crawl as ground truth: AI responses are nondeterministic, and recent research shows substantial variation between repeated queries. A meaningful SOV measurement should sample many prompts repeatedly and report uncertainty. arxiv.org

My shortlist: DeepSmith / AskLab for an off-the-shelf SOV dashboard; Ahrefs if you also want traditional SEO intelligence.

If you tell me whether you mean consumer AI assistants (ChatGPT/Claude/Gemini), AI search (Perplexity/Google AI), or both, I can give you a tighter comparison of the 5–10 vendors, including which ones actually capture raw assistant responses and which can measure voice/chat channels separately.

If you mean AI-search / answer-engine share of voice—tracking how often your brand appears in ChatGPT, Gemini, Claude, Perplexity, etc.—there are several tools built specifically for this.

The category is usually called AI visibility / AI search visibility / LLM visibility. Good options include:

• Profound — monitors how brands appear across AI search/answer engines and measures visibility, mentions, competitors, and citations.
• Peec AI — tracks brand visibility and positioning across AI answers, with prompt-based monitoring.
• Otterly.AI — monitors brand mentions and rankings across AI search engines.
• AthenaHQ — focused on AI-search visibility and competitive intelligence.
• Scrunch AI — tracks how brands are represented across AI assistants and provides visibility analytics.
• Writesonic GEO / AI traffic tools — useful if you want AI visibility plus content/SEO workflows.

If your requirement is specifically “crawl lots of assistant outputs and calculate a defensible share-of-voice number across voice + chat channels”, I’d evaluate Profound, Peec, and Scrunch first.

A key distinction: some products query the assistants themselves, while others primarily analyze web/citation data. For true SOV, you want a system that repeatedly runs a controlled prompt set, records the answer, detects brand mentions + competitors + position/prominence + citations, and calculates something like:

AI SOV = your brand's weighted appearances ÷ all tracked brand appearances

If you tell me which channels you care about (ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, Siri/Alexa, etc.) and whether you need an API, I can compare the available platforms and identify the best fit.

If you mean measuring your brand’s share of voice (SOV) across AI assistant answers (ChatGPT, Gemini, Claude, Perplexity, etc.) and voice assistants, there are a few categories of tools rather than one universal crawler.

AI search / answer engine visibility platforms

These are built specifically around tracking how often brands appear in AI-generated answers:

• Profound — Tracks brand visibility and mentions across AI search experiences, including monitoring prompts and answer outputs.
• Peec AI — Tracks how brands appear in AI-generated answers and compares visibility against competitors.
• Scrunch AI — Focuses on AI search visibility, citations, and brand presence.
• AthenaHQ — Provides AI visibility tracking and optimization insights.

Enterprise SEO / digital intelligence platforms adding AI visibility

Useful if you want AI SOV combined with traditional search metrics:

• Semrush — Has AI visibility features integrated with broader SEO tooling.
• Ahrefs — Strong for search intelligence; AI visibility capabilities are evolving.
• Similarweb — Useful for competitive digital intelligence and market share analysis.

If you need voice assistant monitoring

Voice is harder because assistants often do not expose the same answer data as chat systems. Common approaches include:

• Testing devices/apps directly (e.g., Alexa, Siri, Google Assistant) at scale.
• Using speech analytics platforms for customer voice channels (call center, smart speaker interactions), such as Observe.AI or CallMiner.
• Building an internal prompt-testing framework that records assistant responses and scores brand mentions.

For a custom “AI SOV crawler”

Many companies build an internal system with:

1. A prompt library (thousands of customer-intent queries)
2. Automated runs against AI APIs or browser-based assistants
3. Output parsing:
   • brand mentioned?
   • position/rank in answer?
   • recommendation vs neutral mention?
   • competitors mentioned?
   • citations/source links?
4. Scoring model:
   • AI Share of Voice = your weighted mentions ÷ total weighted brand mentions
   • Track by category, geography, customer intent, and assistant.

Typical stack:

• LLM APIs (for controlled tests)
• Browser automation (for consumer-facing assistants)
• Data warehouse (Snowflake/BigQuery)
• BI dashboard (Tableau/Looker/Power BI)

If you’re measuring marketing competitive SOV, tools like Profound/Peec/Scrunch are the closest fit. If you’re measuring actual customer voice interactions, you’ll need a speech analytics stack.

If you mean “crawl/run representative prompts through AI assistants, capture the answers, and calculate our brand’s share of mentions/recommendations vs. competitors,” there are several good options now.

My shortlist

• Semrush AI Visibility Toolkit — probably the best choice if you already use Semrush. It reports AI Share of Voice, brand mentions, competitors, and visibility across ChatGPT, Google AI Mode, Perplexity, Gemini, etc. www.reddit.com
• Profound — strong enterprise option for monitoring how brands appear across AI answer engines, with competitive intelligence and reporting. www.reddit.com
• Peec AI — more purpose-built for AI visibility/LLM monitoring; tracks mentions, citations and competitors across major AI engines. www.reddit.com
• OtterlyAI — another straightforward AI-search visibility tracker, particularly useful for monitoring mentions/citations and competitors. www.reddit.com
• Allmond — explicitly calculates AI Share of Voice based on recommendations across ChatGPT, Claude, Gemini and Perplexity, including geographic comparisons. allmond.app
• SkuLift — specifically defines SOV as the percentage of generative answers mentioning/citing your brand and measures it across ChatGPT, Claude, Perplexity and Gemini. www.skulift.com

One important distinction

If by “voice channels” you literally mean spoken/voice interactions—e.g. ChatGPT Voice, Gemini voice, Siri, Alexa—you'll want to distinguish that from AI chat/search visibility.

Most current GEO/AEO platforms query the underlying AI/search experiences and analyze the text responses. They generally aren't measuring the actual population of voice conversations or crawling private assistant conversations. That's fundamentally different data.

For a business KPI, I'd define it roughly as:

AI Share of Voice = your brand's weighted mentions/recommendations ÷ all tracked brand mentions/recommendations across a standardized prompt set

Then break it down by assistant × prompt category × competitor × geography × time. That gives you a much more defensible metric than simply counting raw mentions. visibilityvalidator.com

If you tell me which channels you mean by “voice and chat” (e.g. ChatGPT Voice + ChatGPT text + Gemini + Siri/Alexa), I can narrow this to the 2–3 tools that actually cover your stack, including whether they can crawl the responses automatically.

If you mean "Which tools measure how often my brand is mentioned or recommended by AI assistants like ChatGPT, Gemini, Claude, Perplexity, and others?", this has become a new category often called AI visibility, LLM SEO, or share of voice for AI.

Some of the better-known platforms include:

These platforms typically:

• Execute hundreds or thousands of prompts across multiple AI assistants.
• Record whether your brand appears.
• Measure share of voice versus competitors.
• Track sentiment and recommendation quality.
• Show which sources the models cite.
• Monitor changes over time after content or SEO updates.

If you're specifically interested in voice assistants

If "voice channels" means assistants like Siri, Alexa, Google Assistant, or ChatGPT Voice, it's more challenging because there is no universal analytics API. Most vendors approximate voice share of voice by:

• Running scripted voice prompts repeatedly.
• Transcribing responses.
• Measuring brand mentions.
• Comparing results across assistants.

Voice analytics is generally less mature than text-based AI search analytics.

If you're looking for enterprise-grade measurement

For a comprehensive view across both chat and AI search, many larger organizations evaluate:

• Profound
• Scrunch AI
• Peec AI

These tend to focus on competitive benchmarking, prompt libraries, longitudinal tracking, and reporting suitable for marketing and communications teams.

If your goal is different—for example, calculating share of voice from your own AI assistant's conversation logs rather than public AI assistants—I can recommend a different set of analytics tools.

If you mean measuring your brand’s share of voice (SOV) across AI assistant answers (ChatGPT, Claude, Gemini, Perplexity, voice assistants, etc.), there are a few categories of tools rather than one universal crawler. The space is often called AI visibility, AI search optimization (AIO), generative engine optimization (GEO), or LLM brand monitoring.

Some tools that focus on this:

For voice assistants specifically (Alexa, Siri, Google Assistant), measurement is harder because many voice systems do not expose a searchable corpus of responses. Companies usually combine:

• AI answer monitoring tools (for ChatGPT-style interfaces)
• scripted prompt testing
• voice device testing labs
• traditional SEO/local search monitoring (for answers sourced from web indexes)

A typical SOV calculation looks like:

AI Share of Voice (%) =

Your brand mentions or recommendations ÷ Total brand mentions/recommendations across tracked prompts × 100

Common metrics:

• % of prompts where your brand appears
• average position/rank in AI recommendations
• sentiment of AI answer
• citation frequency (which sources AI systems rely on)
• competitor visibility gap
• category ownership (e.g., “best CRM for startups”)

If you are building an internal system rather than buying a SaaS tool, the stack is usually:

1. Prompt library (thousands of category questions)
2. Automated querying of AI platforms where permitted
3. Response extraction
4. Entity/brand detection
5. Ranking + sentiment classification
6. Dashboarding (Looker, Tableau, Power BI, etc.)

One caveat: directly scraping consumer AI assistants can violate platform terms, and many assistants change outputs dynamically. Enterprise approaches usually use approved APIs, monitoring partnerships, or controlled query environments.

If you mean "Which tools measure how often my brand is mentioned or recommended by AI assistants like ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.?", this is a rapidly growing category often called AI search analytics, LLM visibility, or AI share of voice.

Some of the better-known platforms include:

These products generally:

• Run thousands of prompts against multiple AI assistants.
• Measure how frequently your brand appears versus competitors ("share of voice").
• Track rankings within AI-generated answers.
• Identify which sources AI systems appear to rely on.
• Monitor changes over time.
• Surface prompts where your competitors are winning.
• Recommend content improvements to increase AI visibility.

If you want to build this yourself

Many companies create an internal system that:

1. Maintains a library of representative prompts.
2. Queries multiple AI providers (where permitted by their APIs or terms).
3. Extracts mentioned brands, products, and citations.
4. Scores:
   • Share of voice
   • Position in the response
   • Sentiment
   • Citation frequency
   • Recommendation rate
5. Stores historical results in a warehouse for trend analysis.

This gives you complete control over prompt selection and scoring, though results may differ from what end users see in consumer chat interfaces because production assistants often use additional retrieval, personalization, or interface features.

Important limitation

No tool can comprehensively "crawl" the live outputs of all AI assistants in the way a web crawler indexes websites. Consumer AI chats are personalized, non-public, and generated on demand. Current platforms estimate AI share of voice by repeatedly sampling responses across a curated set of prompts, models, locations, and time periods rather than observing every conversation.

If your goal is competitive benchmarking, these sampling approaches are currently the industry standard.

If you're evaluating vendors, I can also compare them on:

• model coverage (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.)
• API availability
• historical trend tracking
• citation analysis
• enterprise integrations (Looker, Snowflake, BigQuery)
• pricing and scale.

If you're trying to measure AI Share of Voice (AI SOV)—how often your brand is mentioned, recommended, or cited by AI assistants like ChatGPT, Gemini, Claude, Perplexity, and Copilot—there are now several specialized platforms. They generally work by running large sets of prompts across AI systems, tracking mentions, sentiment, ranking, and competitor comparisons. None of them can literally "crawl" every user conversation with AI assistants (those conversations are private), so they estimate visibility by testing representative prompts.

Some of the better-known options include:

When evaluating these platforms, look for capabilities such as:

• Prompt library management (thousands of prompts)
• Cross-model testing (ChatGPT, Gemini, Claude, Perplexity, Copilot)
• Share of voice by competitor
• Citation/source tracking
• Geographic and language segmentation
• Trend analysis over time
• API access for dashboards

A typical AI SOV metric looks like:

Brand mentions ÷ Total brand mentions across all tracked competitors over a defined prompt set.

For example, if 5,000 prompts produce:

• Your company: 1,800 mentions
• Competitor A: 1,500
• Competitor B: 1,200
• Competitor C: 500

Your AI Share of Voice would be about 36%.

If you're an enterprise, Profound and Goodie tend to offer the most comprehensive AI-native monitoring. If you're a smaller team or agency, Otterly.AI and Peec AI are often easier to adopt and less expensive.

One important limitation applies to every vendor: no tool has access to the private outputs seen by all users of ChatGPT or other assistants. Instead, these platforms automate prompt testing against public or API-accessible models and use statistically representative prompt sets to estimate AI visibility. That means they measure how your brand is likely to appear for relevant queries, rather than observing real user conversations.

If you mean measuring your brand’s share of voice (SOV) across AI assistant answers (ChatGPT, Claude, Gemini, Perplexity, etc.) and chat/voice search channels, you’re looking for an AI visibility / answer engine optimization (AEO) measurement platform rather than a traditional SEO crawler.

Some tools in this space:

For voice channels specifically (Alexa, Siri, Google Assistant), measurement is harder because those assistants often do not expose the same answer data as chatbots. You may need a combination of:

• conversational analytics platforms,
• voice SEO tools,
• scripted query testing,
• speech recognition testing,
• and brand mention monitoring.

For an enterprise SOV program, the typical workflow is:

1. Build a prompt universe (e.g., “best CRM for startups,” “top accounting software,” “restaurants near me”).
2. Run those prompts repeatedly across AI assistants.
3. Extract:
   • whether your brand appears,
   • ranking/position in the answer,
   • citations/sources used,
   • competitors mentioned,
   • sentiment,
   • recommendation frequency.
4. Calculate AI share of voice:

AI SOV = (your brand mentions / total brand mentions across tracked answers) × 100

If you mean a tool that literally crawls and archives AI assistant outputs at scale, the closest category is currently AI answer monitoring / generative engine optimization (GEO) platforms rather than web crawlers.

For a specific use case (enterprise marketing, SEO team, PR monitoring, voice assistant testing, or competitive intelligence), the best tool choice changes.